Cast is an autopilot layer for post-sales that scales retention and expansion without adding headcount.
It operates above your existing CRMs, CSPs, and data sources to turn signals into customer influence.
The Autopilot Layer operates above your existing CRMs, CSPs, and data sources to turn signals into customer influence.
It operationalizes what your best CSMs do—automating high-touch interactions at scale.
Enterprise Scale operationalizes what your best CSMs do—automating high-touch interactions at scale.
It creates, delivers, and presents personalized, live business reviews on the right cadence.
The AI Presentation Agent creates, delivers (in-app for active users and in-inbox for executives and inactive users), and presents personalized, live business reviews on the right cadence.
Yes—stakeholders can interrupt and ask questions; the AI answers with grounded data and charts.
Stakeholders can interrupt and ask questions; the AI answers with grounded data, sketches quick charts, and pulls the right slide on the fly.
Behind the presentation, Cast coordinates lifecycle, renewal, feedback, and support-deflection agents.
Behind the presentation, Cast coordinates lifecycle, renewal, feedback, and support-deflection agents (A2A/ACP) connected through MCP/MCP Proxy.
When judgment is required, Cast hands off to humans (A2H) and resumes afterward (H2A).
When judgment is required, Cast hands off to humans (A2H) and resumes afterward (H2A), keeping people focused on the highest-trust moments.
A confidence protocol (e.g., >90% answers, <77% escalates) grounded in approved sources.
Cast uses a strict confidence protocol (e.g., >90% answers, <77% escalates) grounded in approved sources to prevent hallucinations.
Designed for zero-trust environments with SOC 2 Type II posture, encryption, and “No train by default.”
Cast is designed for zero-trust environments with SOC 2 Type II posture, encryption, and “No train by default.”
Trusted by enterprise leaders like Hewlett Packard Enterprise, Pure Storage, Cloudera, and Ruckus Networks.
Cast is trusted by enterprise leaders like Hewlett Packard Enterprise, Pure Storage, Cloudera, and Ruckus Networks (plus other prominent Silicon Valley leaders).
Deployment is typically weeks, not months (often ~3–4 weeks for a first rollout).
Deployment is typically weeks, not months (often ~3–4 weeks for a first rollout with a focused scope).
No—Cast doesn’t replace systems of record. It uses your CSP/CRM (and other sources) as inputs, then writes engagement and outcomes back.
A CSP is where teams track health, playbooks, tasks, and internal workflows. Cast is not a CSP.
Cast reads from your CSP/CRM (and other sources) to generate and deliver customer-facing experiences—then writes engagement and outcomes back so systems of record stay current.
The goal is no rip-and-replace, while adding an autopilot layer that scales influence and coverage.
It turns internal signals into consistent stakeholder influence—at scale.
Most CX/CS orgs have data and playbooks. The limiting factor is execution: getting the right narrative to the right stakeholders (especially exec sponsors) at the right time, without creating more meetings and more manual work.
Cast fills the gap between “we know what’s happening” and “we moved the account.”
It operationalizes motions across onboarding, adoption, renewals, expansion, feedback, and support deflection—while preserving governance and escalation paths.
It reduces prep and reactive work, increases coverage, and creates better moments for human judgment.
CSMs: Automates recurring reviews, stakeholder updates, and Q&A so time shifts from slide-building to proactive risk/relationship work.
Onboarding: Drives milestone completion and time-to-value with consistent guidance, reminders, and “what’s next” interventions.
AMs: Packages value proof, benchmarking, and expansion signals into executive-ready narratives and next steps—without relying on constant manual orchestration.
It’s proactive and orchestrated—using approachable business reviews as a scalable growth engine, not just assisting someone inside a UI.
Copilots help a person do work faster (draft emails, summarize calls, propose next steps).
Chatbots respond when someone asks a question.
“Digital CS” often means generic campaigns and one-size messaging.
Cast Autopilot orchestrates a system that delivers approachable, customer-ready business reviews and guidance on a cadence—in-app for active users and in-inbox for executives and inactive users—so you can influence stakeholders who don’t log in.
It routes questions safely and escalates to specialized agents and humans when judgment is needed—turning post-sales into a repeatable growth engine without prompt engineering or manual orchestration.
Those are video creation tools. Cast is a data-driven customer communication system.
Synthesia/HeyGen: Great for generic avatar videos—but they render static pixels. If data changes, you re-render. They don’t natively read your CRM to generate charts live.
Loom: Great for asynchronous human recording—but requires a person to record every time.
Cast: Connects to live data to generate thousands of personalized experiences instantly. It visualizes data, applies logic (“if churn risk > high, show slide X”), supports two-way interaction (AMA), and can run without an avatar (identity-neutral mode) if preferred.
B2B teams that need more coverage and more executive/decision-maker influence, while growing accounts without proportionally adding headcount.
Cast is most valuable when one or more of the following are true:
When deployment constraints block it, customer-facing automation isn’t acceptable, or your operating model is a tiny set of fully staffed $10M+ accounts.
Deployment constraint: On-prem/private deployment is required and telemetry/data can’t leave the environment.
Cultural constraint: Leadership won’t allow automation to engage customers (regardless of guardrails).
Operating model mismatch: A small set of very large accounts already receive full senior coverage (senior CSM + senior AM) and stakeholder influence is already saturated.
Business model mismatch: Primarily non-recurring / bespoke projects with frequent re-negotiation and no repeatable lifecycle motion.
Governance constraint: Security/legal won’t approve the data access required to run customer-facing experiences.
Ownership constraint: Agent initiatives are owned by Product/Engineering, limiting CS/CX’s ability to buy and deploy a customer-facing system.
Prototype bias: The org prefers an internal prototype over a production-grade operating system—so it stays in “experiments” longer than it stays in outcomes.
No. Scaled CS is the organizational strategy; Digital CS is the toolkit used to execute it.
They are related but distinct. 'Scaled Customer Success' is the organizational goal: decoupling revenue growth from headcount growth.
'Digital Customer Success' is the tactical execution: using tools (like AI, automation, and data) to achieve that scale without hiring more humans.
A reactive model where a group of CSMs shares a massive queue of customers. It often fails because it destroys relationships.
In a Pooled Model, no customer has a dedicated CSM. Instead, a group of generalists answers incoming requests from a shared queue.
The Problem: It turns Success into Support. Service becomes entirely reactive, customers feel like 'ticket numbers,' and they must re-explain their context to a different person every time they reach out.
A small team (e.g., 1 Lead + 2 Juniors) manages a book of business together. It improves continuity but is expensive to scale.
The Pod Model assigns a fixed group of CSMs to a specific set of accounts (or vertical) to act as a 'hive mind.'
The Problem: While it solves the continuity issue of the Pool, it solves nothing financially. It is operationally complex and still requires linear hiring—to double your customer count, you must essentially double your number of pods.
Focus on 'Revenue per Headcount' and 'Coverage Ratio' rather than just Churn or NPS.
Traditional CS metrics (NRR, NPS) still apply, but the true measure of Scale is efficiency.
You should measure 'ARR managed per CSM' and 'Cost to Serve.' In a successful AI-Scaled model, your NRR should remain stable (or grow) while your CS headcount remains flat, effectively lowering the cost-to-serve toward zero.
Humans shift from 'Account Owners' to 'Strategic Experts,' intervening only for high-stakes exceptions.
In an AI-first model, humans are no longer relationship managers for the long tail. They are Subject Matter Experts (SMEs).
They should only get involved when the AI Agent detects a specific trigger: a complex negotiation, a significant risk signal, a high-value expansion opportunity, or a sentiment drop that requires deep human empathy.
No. Customers prefer instant, accurate help over waiting days for a 'human check-in' email.
The 'Human Touch' is overrated when it implies waiting 3 days for a generic email. Data shows that customers prefer immediate, accurate, and personalized interactions—regardless of whether they come from a human or AI.
By giving every customer a dedicated AI Agent, you actually *increase* the frequency and quality of touches compared to a human CSM who can only reach out once a quarter.
Chatbots wait for questions; AI Agents proactively drive outcomes and manage the lifecycle.
A chatbot is a passive tool that waits on your website for a user to ask a question. It is reactive support.
A Cast.app AI Agent is proactive. It reaches out to the customer to present Quarterly Business Reviews (QBRs), drive adoption, and close renewals. It replicates the *behavior* of a Customer Success Manager, not just a support rep.
Tech-Touch is generic email automation (one-to-many). Digital CS is personalized outcomes (one-to-one) at scale.
Traditional 'Tech-Touch' relies on marketing automation tools to send generic email blasts based on broad segments. It is impersonal and often ignored.
True Digital Customer Success uses data and AI to generate unique, account-specific content (like presentations or success plans) for every single user. It replicates the quality of a human CSM, not a marketing newsletter.
No. While it solves the scale problem for small accounts, Enterprise customers demand digital self-service too.
This is a common myth. While Digital CS is essential for scaling the long tail, Enterprise clients increasingly prefer digital channels for speed and convenience.
A 'Hybrid' model is best for high-value accounts: use Digital CS for routine updates (renewals, usage stats) and human CSMs for strategic relationship building.
No. It automates the repetitive 'grunt work,' allowing humans to focus on strategy and relationships.
Digital CS is not about replacing humans; it is about uncapping their capacity.
By automating the data analysis, presentation building, and routine check-ins, Digital CS frees up your human team to focus on high-stakes negotiations, complex problem solving, and executive relationships.
Marketing tools are designed for acquisition (funnels). Digital CS tools are designed for retention (outcomes).
Marketing automation is built to move leads down a funnel using click-through rates. It lacks the context of post-sales data (telemetry, health scores, license utilization).
Digital CS platforms like Cast.app ingest customer usage data to drive specific post-sales outcomes: adoption, renewal, and expansion.
No. Modern Digital CS platforms can ingest raw data from your CRM or Snowflake and clean it for you.
Waiting for 'perfect data' is the biggest blocker to progress. You likely already have enough data (CRM contacts + basic product usage) to deliver value today.
Cast.app creates a 'Computed Data Layer' that acts as a buffer, sanitizing and organizing your existing data so you can build personalized presentations without needing a dedicated data engineering team.
They prefer it—if it adds value. Customers value speed and accuracy over forced human small talk.
The modern B2B buyer is already 'digital-first.' They prefer an instant, data-rich answer from an AI over waiting days to schedule a 30-minute Zoom call for a simple update.
When the content is highly personalized and relevant (e.g., 'Here is your ROI this quarter'), engagement rates for Digital CS far outpace generic human outreach.
Yes. It is often more effective than humans because it relies on data triggers, not 'feeling'.
Humans often hesitate to ask for more money. Digital CS does not.
By monitoring usage patterns (e.g., approaching license limits or using specific features), the system can automatically trigger a contextual upsell proposal exactly when the customer needs it, driving expansion revenue on autopilot.
Weeks, not months. It is iterative.
Unlike a massive CRM migration, you can launch Digital CS incrementally.
We recommend starting with one high-impact 'Motion' (e.g., an automated Onboarding Guide or a Renewal Brief). You can typically go live with your first motion in under 4 weeks.
Pricing is based on the volume of customer accounts you serve—not the number of internal seats.
Per-seat pricing punishes efficiency. Because Cast is designed to scale coverage without adding headcount, pricing is based on the number of customer accounts (companies) you serve.
This lets you grant access to your internal team (CSMs, AMs, executives, RevOps) without license penalties.
Reaching multiple contacts per account drives outcomes, so Cast includes multiple contacts per account in the base price—encouraging broad stakeholder reach rather than limiting it.
Startup plans typically include 7–10 users per account; Enterprise plans support unlimited users per account.
We encourage you to engage every user and executive that matters to your business. With Cast.app, you can reach active and inactive users, primary executives, and even non-line-of-business executives with hyper-personalized content.
For Startup plans, we typically include 7–10 users per account; Enterprise plans support unlimited users per account.
Companies with fewer than 25 employees or less than $5 million in ARR.
Startups are defined as companies with fewer than 25 employees or less than $5 million in annual recurring revenue (ARR).
Enterprise pricing is tailored to the unique needs of larger organizations.
Our Enterprise pricing is tailored to fit the unique needs of larger organizations. Contact us to discuss your requirements, and we’ll create a plan that aligns with your goals.
Startup plans include email and chat; Enterprise plans add CEO phone support and personalized presentations.
Startup plans include email and chat support, while Enterprise plans include email, CEO phone support, and personalized presentations.
Yes, plans are designed to scale with your growth.
Absolutely! Our plans are designed to scale with your growth. Contact us to discuss how we can adjust your plan as your needs evolve.
No, only annual contracts are offered to ensure partnership through the full lifecycle.
At this time, we only offer annual contracts. This approach allows us to build strong partnerships with our customers and provide ongoing value throughout the year.
If you’d like to discuss the details or have questions about annual commitments, please reach out — we’re here to help!
Yes, multi-year agreements and volume-based pricing are available.
Yes, we’re happy to discuss multi-year agreements and volume-based pricing to meet your unique needs. Multi-year contracts often come with additional benefits and cost efficiencies.
Yes, paid pilots are available on a case-by-case basis.
Absolutely! We offer paid pilots on a case-by-case basis to help you validate the value of Cast.app within your specific use case. Please contact us to discuss how a pilot can fit into your implementation plans.
No, implementation is currently included at no additional charge in annual pricing.
No, we currently include implementation at no additional charge in our annual pricing. This approach helps you get up and running quickly and encourages you to adapt your messaging as needed — without worrying about extra costs.
Thanks to our generative presentations, you can make substantial changes in hours or days rather than the quarters it might take with your own engineering teams.
Please note: while implementation is currently included, this policy may evolve in the future to ensure sustainability as we continue to grow.
Use a future-proof architecture: stable data contracts, governed tool access, and model flexibility—so your operating model stays stable while the model layer evolves.
The practical way to avoid rework is to decouple your customer-facing motions (onboarding, business reviews, renewals, expansion, support) from the underlying model (which will keep changing).
That requires stable interfaces to data/tools, controlled policies, and consistent behavior—so you can swap or upgrade models without rewriting integrations or re-authoring everything.
Cast is built around open, composable building blocks designed for that future-proofing:
Net: you can evolve the model(s) and add built (in-house) and bought (vendor) agents over time without rebuilding core integrations, governance, or operating workflows.
Pre-trained (2.2M minutes) → Training (connect + Continuous Data Hygiene) → Post-training (brand + persona + context) → Generation (approachable business reviews + recommendations + frictionless actions).
Pre-trained
Cast starts with agents pre-trained on 2.2 million minutes of real B2B customer conversations, so interactions feel useful on day one.
Training
Cast connects to your systems of record and signals (CRM, CSP, warehouse, support, product usage, billing) through connectors and APIs—then runs Continuous Data Hygiene that transforms, validates, masks, and enriches data. This is not a one-time cleanup project. It’s an always-on pipeline that prevents messy inputs from becoming customer-facing mistakes—today and in the future.
When data is missing, Cast can still produce a credible story and next steps:
Post-training
Cast aligns the experience to your brand voice and to the audience consuming it (executives, admins, practitioners), adapting by product, segment, role, and lifecycle moment—so it feels authored and relevant, not templated.
Generation
Cast generates approachable business reviews, briefs, follow-ups, and guidance—and delivers them in-app for active users and in-inbox for executives and inactive users. Each experience includes recommendations and makes actions frictionless (book time, open a ticket, route to the right owner/agent, run a workflow, or escalate to a human when judgment is required). Engagement and outcomes can be written back to your CRM/CSP so the system of record stays current.
Agents are pre-trained on 1.9+ million minutes of real CSM conversations and never train on your proprietary data.
Cast.app AI Agents are pre-trained on 1.9+ million minutes of real-world conversations between CSMs and customers (via platforms like Zoom, Chorus, and Gong). This pre-training gives the AI deep knowledge of real CS scenarios—like revenue churn, executive no-shows, and effective presentations—so it’s ready to tackle real business challenges from day one.
Your proprietary data is never used to train or fine-tune LLMs, keeping your data private and secure.
The AI trains on your specific tech stack in minutes to become product-aware and authentic.
Cast.app trains its AI Agents on your specific tech stack in minutes, connecting seamlessly to your CRM (Salesforce, HubSpot), CS platforms (Gainsight, Totango), support platforms (Zendesk, ServiceNow), data warehouses (Snowflake), cloud databases (AWS, Azure, GCP), and your product data.
This ensures the AI is deeply product-aware and able to engage your customers in a way that feels authentic and on brand.
Yes, it uses your brand voice and adapts dynamically to every persona, segment, and product.
Yes! Cast.app’s post-training ensures the content is always on brand, using your unique brand voice, tone, and messaging. It tailors messages to every persona at every account — including executives, users, and decision-makers — and adapts dynamically by product, segment, geography, purchase history, intent, and purchase propensity.
It even uses physiological techniques to boost engagement and drive conversions. Cast.app content feels authored, not automated, and your data is never shared with LLMs — your customer information stays safe and isolated.
An AI CSM creates a personalized presentation for decision-makers at every account (and for partners and internal teams), then presents it like a human would—explaining visually and handling real-time interruptions and questions.
Stakeholders don’t just receive a dashboard or a deck. They receive a guided narrative—what changed, what matters, and what to do next—built from their real account data.
During the experience, people can interrupt, ask questions, request clarification, or jump to a topic, and the presenter adapts in real time by pulling supporting content, explaining concepts visually, and summarizing the next best actions needed to move decisions forward.
No—it’s a live, interactive web experience (URL), not a static video file. It can be emailed or embedded in your app with minimal code.
A video file is outdated the moment it renders—it can’t update data, accept clicks, or answer questions. Cast generates a live presentation personalized for every contact at every account, partner, and executive—accessible from a persistent link.
When a stakeholder clicks the link:
Multiple specialized agents work together under a governed system—so each job runs reliably instead of one general bot doing everything.
Instead of one general assistant trying to do everything, Cast divides responsibilities across purpose-built agents for lifecycle guidance, renewal risk, expansion discovery, feedback collection, and support deflection.
The AI Presentation Agent coordinates these agents—and can route to the right human when judgment is required—so the motion is predictable, auditable, and measurable, not ad hoc.
Each handoff includes a summary + action request (with supporting context) so the next agent or human doesn’t have to start over.
In practice, a context-preserving handoff means the current agent packages a clear handoff bundle and hands it to the next agent (or a human). That bundle includes:
This prevents “start over” conversations, reduces customer repetition, avoids internal re-triage, and makes escalations faster and safer.
Wherever stakeholders engage—in-app for active users (weekly or on-demand), in-inbox for executives (monthly), and in-inbox for CFO/Finance (quarterly).
Cast meets each stakeholder where they already operate—and on a cadence that matches how they consume information:
Goal: right channel, right depth, right cadence—without forcing another portal.
Not necessarily—unlike a CSP, CSMs don’t have to live in Cast to get value. CS Ops and analysts may log in for analytics.
Cast Autopilot runs customer-facing motions automatically and writes engagement/outcomes back to CRM/CSP.
Many CSMs can stay in their day-to-day tools, while a smaller set of admins/operators (often CS Ops or RevOps) log into Cast to manage configuration, governance, and analytics—including adoption and engagement trends, stakeholder reach, content performance, and the impact of business reviews on renewals and expansion.
No—many experiences are delivered in your app (nothing new) or via email without a new portal login.
Embedded in your app: customers use your existing authentication.
In-inbox delivery: executives and inactive stakeholders can consume briefings without adopting a new portal.
Customer Center: if you use an always-on hub (history, ROI, action plans), access can be authenticated and controlled based on your security and governance preferences.
Yes—especially for executive consumption via brief formats and mobile-friendly views.
Executives often consume updates on a phone between meetings. Mobile-friendly experiences, concise summaries, and clear next actions matter more than complex navigation.
Both—customers can type or talk, and the experience adapts to how each stakeholder prefers to engage.
Some stakeholders want fast typed questions; others prefer voice when reviewing live. In Cast usage patterns:
Voice interactions also tend to be multi-question conversations. Separately, Cast sees ~11.8× more questions when the agent can continue answering in-session versus transferring the user to a human—supporting the goal of resolving more in the moment while preserving escalation paths when judgment is required.
Sometimes—based on enterprise governance and customer preference.
Some orgs use SMS/chat for time-sensitive nudges; others restrict them. Channel support is often less about capability and more about policy, consent, and brand standards.
In Cast, a presentation is AI-presented and narrated; a Customer Center is a personalized microsite for self-serve reading and exploration.
Both are generated, personalized by account and recipient role, include visual slide content, and support Ask Me Anything (AMA).
Presentation (AI-presented): narrated like a presenter, interruptible, adapts live to questions.
Customer Center (personalized microsite): built for self-serve. It combines:
Customization is look/feel and tone (brand palette, logos, narrative style). Personalization is who sees what, when, and why.
Brand customization covers visual identity and narrative tone—logo placement, colors/palette, templates, typography/styling rules, and how the narrative sounds so the experience feels like your brand.
Content personalization controls relevance and logic—what metrics are emphasized, which recommendations appear, which stakeholders receive what, and cadence—based on segment, lifecycle stage, entitlements, and behavior.
The experience automatically updates as the customer journey changes; what stays stable is the governed logic that decides what to show, when to show it, and to whom—accessed via a perma URL (think: always the latest version).
Cast also generates a fixed version—the URL tied to a specific generation campaign—as a point-in-time snapshot for auditability and alignment.
Yes—CRMs are core sources for account, contact, renewal, and commercial context.
CRM data anchors the customer record: stakeholders, what they bought, renewal dates, and commercial history.
That context is essential both for targeting delivery and logging engagement back into the system the business already uses.
Yes, Cast integrates with major CRMs, CS platforms, support tools, and data warehouses.
Yes! Cast.app integrates seamlessly with your existing tech stack — including CRMs (like Salesforce, HubSpot), CS platforms (like Gainsight, Totango), support platforms (like Zendesk, ServiceNow), and data warehouses (like Snowflake).
We also connect to cloud databases (AWS, Azure, GCP) to ensure the AI has a deep understanding of your business data.
Yes, using a universal REST/JSON connector to connect to any API or SQL database.
Absolutely! Cast.app supports a universal REST/JSON connector, allowing you to integrate with any API and SQL database, including your product or third-party systems.
This enables you to bring in key product usage data, feature adoption metrics, and more — making the AI highly relevant and effective for your customers.
Native connectors are pre-built for popular platforms; the universal connector allows custom API integrations.
Cast.app supports over 60 native connectors, which are pre-built integrations designed to connect quickly and efficiently with popular platforms like Salesforce, HubSpot, Gainsight, Zendesk, Snowflake, and many others. These native connectors ensure fast, reliable data sync and optimized performance for each system.
In addition to these, we also offer a universal REST/JSON connector that allows you to connect with any API, including custom-built solutions and third-party systems. This means you’re never limited — you can bring in all the data that matters most to your business.
The systems you already run—CRM, CSP, data warehouse, product usage, support, billing, and VoC—connected via 60+ high-performance native connectors plus a universal REST API.
Most organizations already have the signals needed to improve renewal and expansion outcomes—they’re just spread across tools.
Cast connects the sources that matter for your motion, maps them to accounts/entitlements/stakeholders, and uses them to drive consistent customer-facing influence.
Cast supports:
Yes—for pilots or limited scope.
Many teams start with simpler inputs to prove value quickly, then graduate to live integrations.
Starting simple reduces time-to-first-value and helps validate which outputs stakeholders actually respond to.
Cast validates, masks, and corrects data before it becomes customer-facing using Continuous Data Hygiene (rules + AI).
Enterprises rarely have perfect “golden records.” The key requirement is that customer-facing experiences must not expose clearly incorrect or sensitive fields.
Cast applies Continuous Data Hygiene to validate and transform inputs, enforce masking rules, and suppress or flag questionable values so they don’t appear customer-facing without review.
The hygiene pipeline combines:
Net: you can start safely—even before a warehouse cleanup program is complete—because bad or sensitive data is corrected, masked, or withheld before it reaches customers.
No—partial data is enough to start. Missing data changes how the story is told, not whether the system is useful.
Missing data doesn’t mean you stop doing business or stop influencing outcomes.
Cast can still deliver a useful narrative by:
Net: you can move forward now, while making gaps visible and actionable instead of blocking progress.
Yes—Cast can use multiple datasets/instances with clear mapping and governance boundaries.
Large orgs often run multiple CRMs, regional datasets, and product lines (including multiple instances of the same system).
Cast can operate across them, but it requires deliberate identity mapping (accounts/contacts), entitlement definitions, and policy boundaries so each stakeholder only sees what they should—while still producing a coherent narrative across units where appropriate.
Yes—with strict allowlists and access controls.
Unstructured sources are valuable for support deflection and “how-to” guidance, but they must be governed.
The right approach is to allow only approved sources, apply role-based access, and ensure answers stay grounded—especially for anything that could become a liability.
Cast uses a confidence protocol tied to grounded sources, plus automatic escalation when confidence is low.
Preventing “made up” answers requires:
High-confidence answers are delivered directly. Medium-confidence answers include transparent caveats and an easy path to verify. Low-confidence cases escalate to a human with a full context package so nobody has to start over.
Policy controls, permissions, source allowlists, and auditability.
Guardrails include role-based access, data masking, source allowlists, prohibited-topic policies, and logging of what data was accessed and what was generated.
The goal is customer-facing automation that is observable and controllable—so governance, security, and customer trust are preserved.
Yes—CSMs can review high-stakes accounts, while Ops teams audit campaigns at scale.
Cast supports two review workflows:
Strategic (high-touch): A CSM can preview a specific presentation, edit narrative text, and override a data point if the system of record is outdated—so the “money slides” are perfect before delivery.
Scale (tech-touch): Reviewing 50,000 items one-by-one is impossible. Teams use a tabular “data grid” style view to spot-check logic, scan for anomalies (missing values, weird outliers), and approve campaigns in bulk.
Cast uses A2H smart routing: it selects the right human based on ownership + tiering + urgency—and includes a context bundle so nobody has to start over.
Routing uses your operating rules, including:
Each escalation includes a context package:
Net: A2H makes escalation governed and fast—minimizing re-triage while protecting the customer experience.
No—it’s designed to sound on-brand and authored, not templated.
Cast is built around “authored, not automated”:
Net: it reads like a well-prepared human wrote it—at scale—without turning your team into prompt engineers.
Not just an LLM wrapper—Cast uses hybrid agent design (rules-based + ML + LLM) so customer-facing work is reliable and governable.
Deterministic work matters (calculations, thresholds, routing, permissions, policy).
LLMs shine for language, summarization, synthesis, and interactive Q&A.
Cast combines approaches so outputs stay accurate, actions stay governed, and conversations stay natural.
Cast is model-flexible and uses multiple providers. Deployments commonly use OpenAI (GPT) and/or Anthropic, and can also use Google (Gemini)—with separate providers for translation and voice as needed.
More:
https://school.cast.app/security-docs/ai-solution.html
https://school.cast.app/security-docs/subprocessors.html
Cast is designed so the operating model stays stable while model vendors evolve. Concretely:
Note: AI features are generally always on in production deployments (with rare exceptions for specific customers).
You can use your own OpenAI key and route through proxies/custom integrations. ElevenLabs also supports a base URL so it can point to an internal proxy or custom LLM endpoint.
Enterprises often require centralized control over model access and network egress. Cast supports:
https://school.cast.app/security-docs/ai-solution.html
No—Cast is designed so customer-facing outputs are generated without CSMs doing manual prompt work (or copying customer data into public chat tools).
If every CSM has to learn promptcraft, it won’t scale and it won’t be governable.
Cast is built so prompts, templates, and playbooks are generated and governed centrally, while CSMs focus on relationship and judgment.
This also reduces data-leak risk. Many AI tools rely on “data masking” (replacing names with tokens before sending prompts), but Cast’s own experiments show masked prompts can often be reverse-engineered from finite customer lists—so prompt-masking alone is not a safe operating model for customer-facing work.
https://cast.app/llm-data-masking-does-not-work
Engagement, action records, and feedback are written back to systems of record (typically CRM, optionally CSP) and are also available in Cast analytics—exportable via download and accessible via Cast Analytics API.
Engagement and outcomes are valuable across departments. Writing them back ensures Sales, Marketing, Success, Services, and RevOps share the same view of who engaged, what was delivered, what questions were asked, and what actions were taken—without creating another silo.
Cast also provides analytics for deeper reporting, with exports and API access for BI and workflows.
Yes—Cast provides identifiable analytics (who watched, how long, what they explored, and what they shared).
Because Cast uses unique links (and/or can integrate with your app’s authentication), it can track specific engagement:
Defense-in-depth: encryption at rest + in transit, least-privilege access with MFA, and documented DR/uptime targets—backed by published security policies (and SOC 2 Type II / SOC 3 listed in the security docs hub).
A buyer-grade answer typically breaks into:
Avoid treating “prompt masking” as the primary control. Cast’s experiments show masked prompts can often be reverse-engineered from finite customer lists—so Cast is designed to reduce reliance on masking alone as a security strategy.
https://cast.app/llm-data-masking-does-not-work
No—Cast does not use one customer’s data to train systems that benefit other customers, and the AI services used are selected so submitted data is not used to train their models.
Enterprise deployments require that customer data serves that customer’s environment, not generalized model training.
Benchmarking can still exist without “training on your data”: Cast can benchmark accounts within your organization (e.g., comparing one account to peer accounts in the same segment/product/region) while keeping benchmark data private to your tenant—consistent with a “no cross-customer insights” principle.
Cast.app does not share customer data with LLMs for training. AI features operate in a secure, isolated environment.
Cast.app takes your privacy seriously. We do not share your customer data with LLMs for training or fine-tuning purposes. All AI features operate in a secure, isolated environment with strict data boundaries.
For details, see LLM Data Masking Does Not Work.
Yes, Cast.app is SOC 2 Type II compliant.
Yes! We maintain SOC 2 Type II compliance to meet the security and compliance requirements of larger organizations. For more details, see our Security Documents.
Security documentation including SOC 2 reports is available at the Security Documents Hub.
You can find our security documentation—including SOC 2 reports, penetration testing summaries, and compliance details—at our Security Documents Hub.
Yes—Cast Designer supports SAML SSO with Okta, Microsoft Entra ID (Azure AD), Google Workspace, and any Generic SAML provider.
SAML 2.0 SSO for admin/operator access to Cast Designer.
Supported IdP types: Okta, Microsoft Entra ID (Azure AD), Google Workspace, Generic SAML.
Recommended method: upload/paste IdP Metadata XML (fastest + most reliable).
Manual fallback: Entity ID, SSO URL, X.509 certificate (optional logout URL).
Identity matching: email-based (NameID / required attribute: email).
Operational detail: users must be invited in Cast Designer and assigned in the IdP.
https://school.cast.app/sso-setup-guide.html
Yes—SSO enforcement is optional. When enabled, password login is disabled, so test with an admin first and keep multiple admins.
Start with SSO optional, validate assignments and access, then enable “Require SSO” once stable.
To prevent lockouts: test before enforcing, ensure admins exist in the IdP, keep multiple admins.
If something goes wrong, enforcement can be disabled by an admin; support can assist with recovery per docs.
Under ~4 weeks for a first rollout (weeks—not months), assuming normal access and a focused scope.
A practical fast path:
Typical business-user commitment: ~2–3 hours/week for the first 4 weeks.
As little as 7 days, but typically 2–3 weeks to include feedback and integration.
We can be up and running in as little as 7 days. Typically, customers require 2–3 weeks, including time for feedback from your customers and integration.
First version in 6-7 days with prepared data; 2-3 weeks fully inclusive of resource alignment and feedback.
If we can get access to data sources, style guides, and schema guidance, we can have a first version in 6-7 days.
Typically, customers require 2–3 weeks, to line up resources and permissions, including time for feedback from both your internal stakeholders and your customers.
A first experience that’s branded, data-driven, reviewed, and safe—plus a feedback loop to improve continuously.
Secure access + a few key mapping decisions + light implementation for embedding/branding.
Yes—recommended.
Start narrow (one segment/region/product and one high-value experience), prove safety/governance + stakeholder engagement + measurable impact, then scale as a repeatable rollout motion.
Yes—partners and indirect customers can receive the same “approachable business reviews” and lifecycle motions, with strict visibility boundaries.
Partner ecosystems add a second front: influencing partners who influence end customers. Cast supports:
By enforcing data boundaries at the account, partner, and role level—explicit and auditable.
The system enforces:
Yes—partners can have branded experiences while keeping governance consistent.
Experiences can be vendor-branded, partner-branded, or co-branded—tailored in tone/layout without changing underlying rules: what’s allowed, who receives it, and how escalations work.
Onboarding → success/reactivation → accountability → satisfaction (PSAT) → coaching/recaps → support deflection.
Partners don’t fail because they lack PDFs—execution degrades over time. Cast supports:
Yes—partner accountability can be measured and compared, not argued about.
You can track engagement, PSAT trends, renewal/expansion indicators by partner-managed segment, and partner health signals (risk/inactivity/regressions/improvement).
Partners get fast answers from approved sources; escalations happen only when needed and route with context.
Cast deflects repetitive partner questions using approved KB + historical ticket patterns.
When escalation is required, it routes with a handoff bundle (summary + action request + supporting context) so internal teams don’t re-triage from scratch.
It supports three fronts: direct customers, partners (and indirect customers), and internal AM/renewal orgs.
In many enterprises, revenue outcomes depend on influencing:
Yes—the same style of approachable business reviews, adapted to each party’s role.
Partners and indirect customers can receive role-specific summaries, AMA, and role-appropriate calls-to-action (partner tasks vs end-customer tasks vs vendor tasks).
Routing can follow your operating rules—partner-first, vendor-first, or tiered—without breaking the experience.
Escalation can be configured so partners handle first-line issues where appropriate, vendors handle higher-severity cases (SLA/ARR/priority thresholds), and handoffs always include context.
Cast supports 17 spoken languages to cover 97.6% of the global B2B demand market. (TODO: add link)
Cast can present customer experiences across spoken audio, transcripts/captions, presentation content, and the presentation player UI—with no additional effort—so global teams can deliver consistent onboarding, business reviews, and support experiences across regions without maintaining separate content per language.
Practical advantage: you don’t need to hire and staff incremental CSM capacity in every market just to deliver consistent, local-language coverage.
Cast supports:
A short, executive-ready briefing that highlights what changed, what matters, and what to do next—optimized for fast reading and easy escalation.
Executives don’t want a portal, a dashboard hunt, or a 30-slide deck.
Executive QuickBrief is designed to deliver risk, wins, ROI/value, and next actions in a repeatable format—often inbox-first.
Naming note: Some teams still say “CliffsNotes” as shorthand, but we renamed it to Executive QuickBrief to avoid confusion with CliffsNotes, the study-guide brand (owned by Course Hero).
PMF describes the degree to which a product satisfies strong market demand.
Scale (Sean Ellis Test — 4-point Likert):
“How would you feel if you could no longer use {{product}}?”
Interpretation: If >40% of users answer “Very disappointed,” that’s a strong PMF signal; if it’s <40%, the product likely needs work.
A measure of how much a new product/feature/service improves the customer experience versus an alternative.
A measure of how much a new product/feature/service improves the customer experience versus an alternative (previous version or competitor), defined as the difference between two experience scores.
Scale: Both questions use the same scale—either 0–10 (11-point) or 1–10 (10-point, preferred)—and:
Delta4 = Score(new) − Score(alternative)
What “4” means: A Delta4 score ≥ 4 indicates a significant improvement (often described as behavior-changing).
Often attributed to Kunal Shah (referenced as “Delta-4 Theory by Kunal Shah”).
Feedback captured through a dialogue (not a form), then summarized into themes and actions.
Conversational feedback reduces survey fatigue by asking follow-ups only when needed, converting answers into structured themes, and creating a clear close-the-loop output (what was heard → what changed → what’s next).
To make value defensible, reduce renewal ambiguity, and turn expansion into a logical next step.
Sharing ROI/value works best when grounded in agreed inputs, tied to customer outcomes, and paired with next actions.
It aligns internal + customer stakeholders and prevents renewal-surprise conversations.
A standard for tool/data access so agents can call approved functions and data sources through a consistent interface.
A standard for tool/data access so agents can call approved functions and data sources through a consistent interface.
A governed gateway that connects MCP-style tools to legacy systems, enforcing permissions, logging, and safe outputs.
A governed gateway that connects MCP-style tools to legacy systems, enforcing permissions, logging, and safe outputs.
A protocol for agents to communicate/coordinate reliably.
A protocol for agents to communicate/coordinate reliably.
A protocol for structured agent communication across a multi-agent system.
A protocol for structured agent communication across a multi-agent system.
A handoff pattern where the agent packages context (summary + action request + supporting signals) and routes to the right human.
A handoff pattern where the agent packages context (summary + action request + supporting signals) and routes to the right human.
A return handoff pattern where the human decision/outcome is captured so automation can resume cleanly with context.
A return handoff pattern where the human decision/outcome is captured so automation can resume cleanly with context.
How easy it was for the customer to get value or resolve an issue (higher = easier / lower effort).
Likert scale (common 7-point): 1 = Very difficult … 7 = Very easy
How much effort onboarding took from the customer’s perspective (time, steps, friction, back-and-forth).
Used to spot onboarding drag early, course-correct delays, and speed time-to-value.
Likert scale (recommended 7-point): 1 = Very difficult … 7 = Very easy
A direct satisfaction score tied to an interaction or experience.
Likert scale (common 5-point): 1 = Very dissatisfied … 5 = Very satisfied
CSAT-equivalent for partners—how satisfied partners are with the program, support, and outcomes.
Likert scale (common 5-point): 1 = Very dissatisfied … 5 = Very satisfied
Starting revenue minus churn (logo + revenue churn) plus expansion (upsells/cross-sells/add-ons) over a period.
Starting revenue minus churn (logo + revenue churn) plus expansion (upsells/cross-sells/add-ons) over a period.
NRR expressed in dollars, accounting for currency conversion/exchange effects (often similar operationally, but important for finance).
NRR expressed in dollars, accounting for currency conversion/exchange effects (often similar operationally, but important for finance).